{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "596ca3a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "58e5ef7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_image,train_label),(test_image,test_label)=tf.keras.datasets.fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9deb716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (60000,))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image.shape,train_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e8268f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 28, 28), (10000,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image.shape,test_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a288a792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd76a1b5250>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_image[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d10749d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(train_image[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "127562e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "356abb58",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image=train_image/255\n",
    "test_image=test_image/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1de4089a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f88bc79",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "             optimizer='adam',\n",
    "             metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "57ed181c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6214 - acc: 0.7841\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fd76ac96070>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_image,train_label,epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a963c7f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 1ms/step - loss: 0.4190 - acc: 0.8485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.4189933240413666, 0.8485000133514404]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_image,test_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70fe4f99",
   "metadata": {},
   "source": [
    "# 独热编码 categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "498cbe66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "164a2a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label_onehot=tf.keras.utils.to_categorical(train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03f33e92",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_label_onehot=tf.keras.utils.to_categorical(test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9fae9746",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "\n",
    "model.add(tf.keras.layers.Dense(128,activation='relu'))\n",
    "\n",
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "237a72db",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "             optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
    "             metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07cf3837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2283 - acc: 0.9125 - val_loss: 0.3521 - val_acc: 0.8798\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2230 - acc: 0.9158 - val_loss: 0.3463 - val_acc: 0.8780\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2168 - acc: 0.9172 - val_loss: 0.3341 - val_acc: 0.8840\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2091 - acc: 0.9199 - val_loss: 0.3484 - val_acc: 0.8834\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2025 - acc: 0.9221 - val_loss: 0.3632 - val_acc: 0.8877\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1965 - acc: 0.9240 - val_loss: 0.3770 - val_acc: 0.8835\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1926 - acc: 0.9263 - val_loss: 0.3796 - val_acc: 0.8883\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1856 - acc: 0.9278 - val_loss: 0.3659 - val_acc: 0.8882\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1806 - acc: 0.9315 - val_loss: 0.3636 - val_acc: 0.8872\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1776 - acc: 0.9327 - val_loss: 0.3904 - val_acc: 0.8835\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(train_image ,train_label_onehot,epochs=10,\n",
    "         validation_data=(test_image,test_label_onehot)\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3f238936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "98a6f21e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd76a325b20>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history.get('loss'),label='loss')\n",
    "plt.plot(history.epoch, history.history.get('val_loss'),label='val_loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6384950e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd76ac9c400>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history.get('acc'),label='acc')\n",
    "plt.plot(history.epoch, history.history.get('val_acc'),label='val_acc')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c29374",
   "metadata": {},
   "source": [
    "上图看出来是过拟合了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c167b499",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict=model.predict(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b14946c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 10),\n",
       " array([3.2852258e-05, 7.6269171e-14, 9.9544996e-01, 6.5557231e-08,\n",
       "        4.3599335e-03, 1.5156931e-11, 1.5711442e-04, 1.8408593e-13,\n",
       "        5.4371402e-10, 5.9668735e-14], dtype=float32))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict.shape,predict[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "08bf8580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predict[1])#找出最大的值的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "98a641f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a16805d4",
   "metadata": {},
   "source": [
    "2.10 结束"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8e22a3ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(32,activation='relu'))\n",
    "model.add(tf.keras.layers.Dropout(0.5))\n",
    "model.add(tf.keras.layers.Dense(32,activation='relu'))\n",
    "model.add(tf.keras.layers.Dropout(0.5))\n",
    "model.add(tf.keras.layers.Dense(32,activation='relu'))\n",
    "model.add(tf.keras.layers.Dropout(0.5))\n",
    "model.add(tf.keras.layers.Dense(10,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "65ef040a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "             optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
    "             metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3bb7e09a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 1.8547 - acc: 0.3013 - val_loss: 0.8926 - val_acc: 0.6312\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 1.2375 - acc: 0.4895 - val_loss: 0.8457 - val_acc: 0.6335\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 1.1570 - acc: 0.5219 - val_loss: 0.8268 - val_acc: 0.6471\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 1.1170 - acc: 0.5420 - val_loss: 0.8213 - val_acc: 0.6423\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 1.0900 - acc: 0.5481 - val_loss: 0.8238 - val_acc: 0.6374\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 1.0760 - acc: 0.5555 - val_loss: 0.7997 - val_acc: 0.6583\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0604 - acc: 0.5707 - val_loss: 0.7921 - val_acc: 0.6810\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 1.0325 - acc: 0.5832 - val_loss: 0.7830 - val_acc: 0.6961\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 1.0297 - acc: 0.5858 - val_loss: 0.8126 - val_acc: 0.6356\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 1.0378 - acc: 0.5887 - val_loss: 0.7896 - val_acc: 0.6791\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(train_image ,train_label_onehot,epochs=10,\n",
    "         validation_data=(test_image,test_label_onehot)\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d196934d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd74d8c9850>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history.get('loss'),label='loss')\n",
    "plt.plot(history.epoch, history.history.get('val_loss'),label='val_loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "353ed091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd74437ca00>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch, history.history.get('acc'),label='acc')\n",
    "plt.plot(history.epoch, history.history.get('val_acc'),label='val_acc')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f5d0f92",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
